Semantic embedding: Any zero-shot task like recognition or tagging requires semantic information of classes. This semantic information works as a bridge among seen and unseen classes. The common way to preserve the semantic information of a class is by using a one-dimensional vector. The vector space that holds semantic information of classes is called `semantic embedding space'. Visually similar classes reside in a close position in this space. The semantic vector of any class can be generated both manually or automatically. The manually generated semantic vectors are often called attributes [1]}, [2]}. Although attributes can describe a class with less noise (than other kinds of embeddings), those are very hard to obtain because of manual annotations. As a workaround, automatic semantic embedding can be generated from a large corpus of unannotated text like (Wikipedia, news article, etc.) or hierarchical relationship of classes in WordNet [3]}. Some popular examples of such kind of semantic embeddings are word2vec [4]}, GloVe [5]}, and hierarchies [6]}. As generated by an unsupervised manner, such embeddings become noisy but provide more flexibility and scalability than manual vectors.
